7 дней назад
Senior Applied Scientist (AI)
160 900 - 257 100$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Senior Applied Scientist (AI): Building LLM post-training pipelines, reward models, and online evaluation systems for agentic AI powering large-scale home-shopping experiences with an accent on reinforcement fine-tuning, preference learning, and production-grade GPU training. Focus on designing generalizable reward functions, assessing live agent trajectories, and translating state-of-the-art research into reliable production impact.
Location: Remote within the United States; employees may live in any of the 50 states, with limited exceptions.
Salary: $160,900–$257,100 annually in specified states, or $152,900–$244,300 annually in another specified group of states. Equity awards are also available.
Company
is a large U.S. real estate platform building technology for buying, selling, financing, and renting homes.
What you will do
- Own end-to-end LLM post-training pipelines, including SFT, DPO, and RFT/GRPO, on GPU infrastructure.
- Build and train multi-category preference-based reward models that produce scalar signals.
- Design on-policy and online evaluation for live agent trajectories using LLM-as-a-Judge frameworks.
- Convert offline evaluation rubrics into generalizable reward functions for production traces.
- Drive ambiguous technical initiatives across product, engineering, science, platform, and data teams.
- Provide technical leadership and mentorship while bringing advanced AI research into production.
Requirements
- Master’s degree or higher in Computer Science or a related field.
- Hands-on experience with LLM post-training and reinforcement learning fine-tuning, including SFT, DPO, and RFT/GRPO.
- Experience running end-to-end training runs on GPU infrastructure.
- Strong knowledge of generative AI, foundation models, transformers, reinforcement learning, and preference learning.
- Strong programming skills in Python and experience with PyTorch or TensorFlow.
- Ability to independently solve ambiguous problems and provide technical leadership to scientists and machine learning engineers.
Nice to have
- PhD in Computer Science, Machine Learning, or a related field.
- Experience evaluating agentic AI through multi-turn trace analysis, LLM-as-a-Judge, offline evaluations, and online monitoring.
- Published research in post-training, RLHF/RLAIF, preference learning, or reward modeling.
- Experience with GPU training platforms such as Databricks or Fireworks.
Culture & Benefits
- Distributed work through Cloud HQ, with flexibility to work from a productive location within the permitted U.S. locations.
- Work on agentic AI technologies supporting search, personalized guidance, offer strategy, and financing.
- Opportunity to contribute to systems operating at hundreds of millions of requests per day.
- Equity awards are available in addition to base salary.
- Inclusive workplace committed to equal employment opportunity and employee growth.
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